Background: We addressed the definition of limits of error of %CD4+ and CD4+ counts (AbsCD4+) typical of laboratories of excellence, as well as the grading of laboratories based on the decision to take these limits as boundaries of unacceptable data. Methods: We studied the 99.9% confidence intervals of the means of 24 human immunodeficiency virus (HIV)+ and HIV- blood samples analyzed by 18 laboratories of the Liguria Region Quality Assessment Program (Liguria Region QALI. Regression equations of lower M) and upper (L2) confidence limits over the means of data cleared of unusual results were used to interpolate limits of error for mean values in the tested range. Results: L1 and L2 were symmetric around the mean and a single absolute difference (Abs Res) between the limits and the mean was found. Abs Res significantly increased over mean values (P = 0.0005 for %CD4+, P < 0.0001 for AbsCD4+). Limits were compatible with errors shown with blind replicates. Unacceptable results, outside the limits, accounted for 25% and 30% of %CD4+ and for 18% and 35% AbsCD4+ in the Liguria Region QALI and in the Piemonte Region QA Program, respectively. Limits interpolated over the median showed a similar grading. A comparable fraction of unacceptable data was also found with the method used in the U.K. National External Quality Assessment Scheme (NEQAS) immune monitoring scheme. Conclusions: We propose the general use of these regression equations to determine bounds for unacceptable data in proficiency testing and to identify laboratories of excellence.

Background: We addressed the definition of limits of error of %CD4+ and CD4+ counts (AbsCD4+) typical of laboratories of excellence, as well as the grading of laboratories based on the decision to take these limits as boundaries of unacceptable data. Methods: We studied the 99.9% confidence intervals of the means of 24 human immunodeficiency virus (HIV)+ and HIV- blood samples analyzed by 18 laboratories of the Liguria Region Quality Assessment Program (Liguria Region QALI. Regression equations of lower M) and upper (L2) confidence limits over the means of data cleared of unusual results were used to interpolate limits of error for mean values in the tested range. Results: L1 and L2 were symmetric around the mean and a single absolute difference (Abs Res) between the limits and the mean was found. Abs Res significantly increased over mean values (P = 0.0005 for %CD4+, P < 0.0001 for AbsCD4+). Limits were compatible with errors shown with blind replicates. Unacceptable results, outside the limits, accounted for 25% and 30% of %CD4+ and for 18% and 35% AbsCD4+ in the Liguria Region QALI and in the Piemonte Region QA Program, respectively. Limits interpolated over the median showed a similar grading. A comparable fraction of unacceptable data was also found with the method used in the U.K. National External Quality Assessment Scheme (NEQAS) immune monitoring scheme. Conclusions: We propose the general use of these regression equations to determine bounds for unacceptable data in proficiency testing and to identify laboratories of excellence.